Member of Technical Staff, Exceptional Generalist (Remote)

About the role

This is a globally remote opportunity. We're seeking exceptional generalist engineers who can work across the entire vLLM stack: from low-level GPU kernels to high-level distributed systems. This role is designed for self-directed, autonomous individuals who can identify the highest-leverage problems and solve them end-to-end without constant guidance. You'll work asynchronously with our San Francisco headquarters while maintaining full ownership of critical infrastructure.

Responsibilities

• Push the boundaries of LLM and diffusion model serving. Work at the core of vLLM to optimize how models execute across diverse hardware and architectures.
• Write the low-level kernels and optimizations that make vLLM the fastest inference engine in the world, running on hundreds of accelerator types.
• Build the distributed systems that power inference at global scale—design foundational layers enabling vLLM to serve models across thousands of accelerators with minimal latency.
• Build the operational backbone for cluster management, deployment automation, and production monitoring that enables teams worldwide to serve AI models without friction.

Requirements

• Bachelor's degree or equivalent experience in computer science, engineering, or similar
• Demonstrated ability to work autonomously and drive projects to completion without close supervision
• Excellent asynchronous communication skills and ability to collaborate effectively across time zones
• Strong track record of shipping high-impact work in complex technical environments
• Deep expertise in at least one of: systems programming, GPU/accelerator programming, distributed systems, or ML infrastructure
• Core Technical Depth (strong in at least two): CUDA kernels or equivalent (Triton, TileLang, Pallas) with deep understanding of GPU architecture, High-performance distributed systems in Rust, Go, or C++, Python with PyTorch internals and LLM inference systems (vLLM, TensorRT-LLM, SGLang), Kubernetes, container orchestration, and infrastructure-as-code at scale, Transformer architectures, KV-cache memory management, and model serving

Qualifications

• Contributions to vLLM or other major open-source ML/systems projects
• Experience with multiple accelerator platforms (NVIDIA, AMD, TPU, Intel)
• Knowledge of quantization techniques, ML-specific kernel optimization, or compiler technologies
• Track record of improving system reliability and performance at scale
• Written widely-shared technical blogs or impactful side projects in the ML infrastructure space